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Found 9 papers, 2 papers with code
Destination-Constrained Linear Dynamical System Modeling in Set-Valued Frameworks
Directional motion towards a specified destination is a common occurrence in physical processes and human societal activities.
Color Image Denoising Using The Green Channel Prior
In this paper, we propose a simple and effective one step GCP-based image denoising (GCP-ID) method, which aims to exploit the GCP for denoising in the sRGB space by integrating it into the classic nonlocal transform domain denoising framework.
A Comparison of Image Denoising Methods
In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications.
A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance.
Randomized Online CP Decomposition
The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions.
Sparse Density Estimation with Measurement Errors
It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.
Color Image and Multispectral Image Denoising Using Block Diagonal Representation
In this paper, we mainly investigate the influence and potential of representation at patch level by considering a general formulation with block diagonal matrix.
A Brief Review of Real-World Color Image Denoising
Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.
